VOLUMETRIC INTERPRETATION OF MULTI-SPECTRAL SENSOR DATA AS WELL AS OTHER IMAGE ANALYSIS TASKS INVARIABLY INVOLVE A SEGMENTATION PREPROCESSING STEP. THE AIM OF THIS PROPOSAL IS TO IMPLEMENT A FAST (REAL-TIME) AND UNIVERSAL SEGMENTATION ALGORITHM FOR MULTI-SENSOR AND MULTISCALE IMAGE DATA. THE PROPERTIES OF THIS ALGORITHM ARE THE FOLLOWING. IT IS MULTISCALE AND PYRAMIDAL. IN OTHER WORDS, IT WILL NOT ONLY COMPUTE ONE SEGMENTATION, BUT A HIERARCHY OF SEGMENTATIONS FROM FINE TO COARSE SCALES. THE COARSER SEGMENTATION WILL BE DEDUCED FROM THE FINER ONE BY "MERGING" OPERATIONS, GIVING THUS A PYRAMIDAL STRUCTURE TO THE COMPUTATIONS. MOREOVER WITHIN EACH SCALE, VARIOUS SENSORS DATA IS INTEGRATED "FUSED". AS A CONSEQUENCE OF THIS PYRAMIDAL STRUCTURE, THE ALGORITHM WILL BE O(N) -A LINEAR TIME ALGORITHM (ON A SEQUENTIAL COMPUTER), THAT IS, THE COMPUTATIONAL TIME WILL BE EXACTLY PROPORTIONAL TO THE SIZE OF THE DATUM OR (LOG N) IN CONCURRENT IMPLEMENTATION. THE ALGORITHM IS UNIVERSAL, IT DOES NOT DEPEND ON ANY A PRIORI KNOWLEDGE ABOUT IMAGE STATISTICS. THE ALGORITHM IS WELL-POSED AND CONVERGES TO THE PROPER SOLUTION.